As in previous works [1]}, [2]}, we utilize an autoencoder architecture to jointly train the encoder and the decoder.
We propose a fully convolutional encoder \(f_{\theta }\)  and decoder \(g_{\phi }\)  architecture as shown in Fig. REF .
In the architecture, \(C\)  refers to the number of channels in the output tensor of the convolution operation.
\(C_{\text{out}}\)  refers to the number of channels in the final output tensor of the encoder \(f_{\theta }\) , which controls the number of channel uses \(k\)  per image.
The “Pixel shuffle" module, within the “Residual block upsample" module, is used to increases the height and width of the input tensor by reshaping the it, such that the channel dimensions are reduced while the height and width dimensions are increased.
This was first proposed in [3]} as a less computationally expensive method for increasing the CNN tensor dimensions without requiring large number of parameters, like tranpose convolutional layers.
The GDN layer refers to generalised divisive normalization, initially proposed in [4]}, and has been shown to be effective in density modeling and compression of images.
The Attention layer refers to the simplified attention module proposed in [5]}, which reduces the computation cost of the attention module originally proposed in [6]}.
The attention mechanism has been used in both [5]} and [6]} to improve the compression efficiency by focusing the neural network on regions in the image that require higher bit rate.
In our model, this will allow the model to allocate channel bandwidth and power resources optimally.
\(g_\mathcal {C}\)  refers to the two quantization strategies, which we will introduce next.
